Interpretive biases for one's own behavior and physiology in high-trait-anxious individuals and repressors.

The experiments reported here were designed to test predictions from a cognitive theory of personality proposed by M. W. Eysenck (1997). According to that theory, many of the observed differences between individuals high in trait anxiety and repressors (individuals low in trait anxiety and high in social desirability) depend on underlying individual differences in cognitive biases. It follows from the theory that high-anxious individuals should have an interpretive bias for their own behavior in social situations, that is, they exaggerate how anxious it is. In contrast, repressors should have an opposite interpretive bias for their own behavior, that is, they underestimate how anxious it is. Evidence consistent with these predictions was obtained in Experiments 1 and 2. Implications of these findings for cognitive theories of personality are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Benefitting from High-κ Spacer Engineering in Balistic Triple-Gate Junctionless FinFET- a Full Quantum Study

In this paper, a numerically comprehensive investigation have been performed in order to propose a high-κ spacer triple-gate junctionless FinFET (HKS     

TAMER: an adaptive task allocation method for aging reduction in multi-core embedded real-time systems

The Journal of Supercomputing - Technology scaling has exacerbated the aging impact on the performance and reliability of integrated circuits. By entering into nanotechnology era in recent years,...     

An Investigation of Mechanical Properties of Polyurethane Nanocomposites with Various Silicas: Experimental Study and Modeling of Finite Deformation Response

It is well-known that polymer nanocomposites can bring about superior mechanical, thermal, optical, physical, and chemical properties in comparison with pure polymers. In this study, different contents of unmodified silica nanoparticles (Si-Un), surface modified nano-silica by octylsilane (Si-OS), and surface modified nano-silica by polydimethylsiloxane (Si-PDMS) are added to the polyurethane (PU) matrix and their effects on the physical properties of the polymer examined. The experimental results indicate that most of the nanocomposites have a higher tensile strength and elongation. In addition, hyperelastic energy function models have been used to model the stress-strain relation of the nanocomposites. In this study, Mooney-Rivlin, neo-Hookean, Rivlin general polynomial, and Davies-De Thomas (DDT) models have been investigated, possessing respectively, two, one, eight, and three constants to be determined. The differential evolution (DE) optimization method, a strong heuristic optimization algorithm, has been used to find the constants; in which the absolute summation of the differences between the models’ predictions and experimental data is taken into account as the objective function and the models’ constants are considered as the decision variables. Moreover, equation constants are found by using regression, an indicator of DE optimization superiority. The results show that even though the Rivlin general polynomial model provides the most accurate prediction, the DDT model, consisting of three constants, can be considered as the most acceptable one.     

Biomimetic Approach for the Production of 3D Woven Spherical Composite Applied in Apparel Protection and Performance

Applied Composite Materials - Current sports bras are typically manufactured via a cut and sew process resulting in a high volume of post-production material waste. Seams derived from this cut and...     

Multi-physics modeling of doxorubicine binding to ion-exchange resin in blood filtration devices

A group of drugs used in intra-arterial chemotherapy (IAC) have intrinsic ionic properties, which can be used for filtering excessive drugs from blood in order to reduce systemic toxicity. The ion-exchange mechanism is utilized in an endovascular Chemofilter device which can be deployed during the IAC for capturing ionic drugs after they have had their effect on the tumor. In this study, the concentrated solution theory is used to account for the effect of electrochemical forces on the drug transport and adsorption by introducing an effective diffusion coefficient in the advection–diffusion–reaction equation. Consequently, a multi-physics model coupling hemodynamic and electrochemical forces is developed and applied to simulations of the transport and binding of doxorubicine in the Chemofilter device. A comparison of drug adsorption predicted by the computations to that measured in animal studies demonstrated the benefits of using the concentrated solution theory over the Nernst–Plank relations for modeling drug binding.     

Modulators of Macrophage Polarization Influence Healing of the Infarcted Myocardium

To diminish heart failure development after acute myocardial infarction (AMI), several preclinical studies have focused on influencing the inflammatory processes in the healing response post-AMI. The initial purpose of this healing response is to clear cell debris of the injured cardiac tissue and to eventually resolve inflammation and support scar tissue formation. This is a well-balanced reaction. However, excess inflammation can lead to infarct expansion, adverse ventricular remodeling and thereby propagate heart failure development. Different macrophage subtypes are centrally involved in both the promotion and resolution phase of inflammation. Modulation of macrophage subset polarization has been described to greatly affect the quality and outcome of healing after AMI. Therefore, it is of great interest to reveal the process of macrophage polarization to support the development of therapeutic targets. The current review summarizes (pre)clinical studies that demonstrate essential molecules involved in macrophage polarization that can be modulated and influence cardiac healing after AMI.